Photovoltaic Stand-Alone Systems Using an Artificial Neural Network-Based Intelligent Control System
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Abstract
This study introduces an adaptive artificial neural network (ANN)-based control system to enhance the efficiency of stand-alone photovoltaic (PV) systems under dynamic environmental conditions. Traditional maximum power point tracking (MPPT) methods, such as perturb and observe (P&O) and incremental conductance (INC), are hindered by slow convergence and oscillations. The proposed approach utilizes a hybrid ANN architecture with hyperbolic tangent (tanh) and rectified linear unit (ReLU) activation functions in a 6-3 neuron hidden layer structure, enabling real-time prediction of the optimal voltage (V_mpp). Integrated with a PID-controlled DC-DC boost converter, the system seamlessly transitions between the solar harvesting, battery charging, and load supply modes. Trained on 10,000 environmental samples (irradiance: 150–1000 W/m² and temperature: 25–50°C) using the Levenberg-Marquardt algorithm, the ANN achieved 99.2% tracking accuracy with a mean squared error (MSE) of 1.73×10⁻⁵ in 200 epochs. MATLAB/Simulink simulations demonstrated superior performance, surpassing P&O by 4.1% and INC by 3.2%, while maintaining a voltage ripple below 1.5%. Key innovations include the hybrid ANN design that mitigates saturation effects, adaptive PID tuning for minimal oscillations, and a three-mode converter that ensures a stable 24 V load voltage during irradiance fluctuations. This work underscores the potential of machine learning in advancing renewable energy systems, offering a computationally efficient and hardware-ready solution for off-grid applications with enhanced reliability and precision.
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